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I’ve been speaking at several events recently giving practical advice on getting started with AI projects. There is a huge chasm between high level inspirational business pieces on all the usual sites1 that business leaders read and the “getting started in AI” guides that pretty much start with installing Tensorflow. There was nothing aimed at the non-AI CTO who didn’t want to fall behind. Nothing to indicate to them how to start a project, what talent they’d need or even which problems to start with. Sure, there are a lot of expensive consulting companies out there, but this knowledge shouldn’t be hidden.

This time last year, I sat down with David Kelnar of MMC Ventures and we talked about why so many AI projects don’t succeed. He asked me to contribute some ideas to be included in the new State of AI report for 2019, to which I gladly agreed. It soon became clear that to do this justice, it was more than just a chapter, and the MMC AI Playbook was born, which we recently launched. Contributing to this amazing publication took a lot of time and research, and this blog was the thing that had to give.

If you are trying to find the right time to start your first project and need help on where to begin, please take a look at the playbook. Here’s a taster, based on talks I gave at Austin Fraser’s #LeadersInTech event and the Barclays AI Frenzy event both in July 2019.

It’s not often that I feel the need to write a reactionary post as mainly the things that tend to inflame me are usually by design. However today I read something on LinkedIn that caused a polarisation in debate within a group of people who should really appreciate learning from different data: Data Scientists.

What was interesting was how the responses fell neatly into one of two camps: the first praising the poster for speaking out and saying this, supported by nearly an order of magnitude more likes than the total number of comments, and the second disagreeing and pointing out that it can work. What has been lost in this was that “can” is not synonymous with “always” – it really needs a good team and better explanation than many companies sometimes use. What irked me most about the whole thread was the accusation that people doing data science with agile obviously “didn’t understand what science was”. I hate these sweeping generalisations and I really do expect a higher standard of debate from anyone with either “data” or “science” anywhere near their profile. Continue reading Agile Data Science: your data point is probably an outlier

This is part 3 of my summary of ReWork Deep Learning London September 2018. Part 1 can be found here, and part 2 here.

Day 2 of rework started with some fast start up pitches. Due to a meeting at the office I missed all of these and only arrived at the first coffee break. So if you want to check out what 3D Industries, Selerio, DeepZen, Peculium and PipelineAI are doing check their websites. Continue reading ReWork Deep Learning London September 2018 part 3

September is always a busy month in London for AI, but one of the events I always prioritise is ReWork – they manage to pack a lot into two days and I always come away inspired. I was live-tweeting the event, but also made quite a few notes, which I’ve made a bit more verbose below. This is part one of at least three parts and I’ll add links between the posts as I finish them. Continue reading ReWork Deep Learning London September 2018 part 1

ImageNet has been a deep learning benchmark data set since it was created. It was the competition that showed that DL networks could outperform non-ML techniques and it’s been used by academics as a standard for testing new image classification systems. A few days ago an exciting paper was published on arxiv for training ImageNet in four minutes. Not weeks, days or hours but minutes. This is on the surface a great leap forward but it’s important to dig beneath the surface. The Register sub headline says all you need to know:

If you’ve read pretty much any other of my artificial intelligence blog posts on here then you’ll know how annoyed I am when the slightest advance in the achievements of AI spurs an onslaught of articles about “thinking machines”, that can reason and opens up the question of robots taking jobs and eventually destroying us all in some not-to-be-mentioned1 film franchise style. Before I get onto discussing if and when we’ll get to a Detroit Become Human scenario, I’d like to cover where we are and the biggest problem in all this. Continue reading Thinking machines – biological and artificial

At the ReWork Retail and AI Assistants summit in London I was lucky enough to interview Kriti Sharma, VP of AI and Robotics at Sage, in a fireside chat on AI for Good. Kriti spoke a lot about her experiences and projects not only in getting more diverse voices heard within AI but also in using the power of AI as a force for good.

We discussed the current state of AI and whether we needed legislation. It is clear that legislation will come if we do not self-police how we are using these new tools. In the wake of the Cambridge Analytica story breaking, I expect that there will be more of a focus on data privacy laws accelerated, but this may bleed into artificial intelligent applications using such data. Continue reading Democratising AI: Who defines AI for good?

By now, the majority of people who keep up with the news will have heard of Cambridge Analytica, the whistle blower Christopher Wylie, and the news surrounding the harvesting of Facebook data and micro targeting, along with accusations of potentially illegal activity. In amongst all of this news I’ve also seen articles that this is the “awakening ” moment for ethics and morals AI and data science in general. The point where practitioners realise the impact of their work.

Day 2 had a new host for track A in the form of David D’Souza from CIPD. His opening remarks quoted Asimov and Crichton and encouraging us not be magicians and to step back and think about what we should do rather than just what we could. Continue reading AI Congress London 2018 Day 2